


import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase		   =			2




class A    :






      def __init__(   self		:	List[Any]     ,  *,  # begin keyword-only arguments
          lowercase_		:	Any="<s>"     ,  lowercase_		:	str="<pad>"     ,  lowercase_		:	List[Any]="</s>"     ,  lowercase_		:	Optional[int]="<unk>"     ,  lowercase_		:	List[str]=None     ,  )   ->		Dict:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =bos, unk, pad, eos
             _lowerCamelCase			:						List[Any]            =[]
             _lowerCamelCase			:						List[str]            =[]
             _lowerCamelCase			:						Tuple            ={}
             _lowerCamelCase			:						List[Any]            =self.add_symbol(UpperCamelCase__					)
             _lowerCamelCase			:						List[str]            =self.add_symbol(UpperCamelCase__					)
             _lowerCamelCase			:						Any            =self.add_symbol(UpperCamelCase__					)
             _lowerCamelCase			:						str            =self.add_symbol(UpperCamelCase__					)
             if extra_special_symbols:
                    for s in extra_special_symbols:
                           self.add_symbol(UpperCamelCase__					)
             _lowerCamelCase			:						Optional[int]            =len(self.symbols					)






      def __eq__(   self		:	Any     ,  lowercase_		:	Dict					)   ->		Optional[int]:
             """simple docstring"""




             return self.indices == other.indices






      def __getitem__(   self		:	Union[str, Any]     ,  lowercase_		:	Union[str, Any]					)   ->		List[Any]:
             """simple docstring"""




             if idx < len(self.symbols					):
                    return self.symbols[idx]
             return self.unk_word






      def __len__(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             return len(self.symbols					)






      def __contains__(   self		:	int     ,  lowercase_		:	List[Any]					)   ->		List[str]:
             """simple docstring"""




             return sym in self.indices






      @classmethod
      def 			lowerCamelCase			(   cls		:	int     ,  lowercase_		:	Optional[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =cls()
             d.add_from_file(UpperCamelCase__					)
             return d






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	List[str]     ,  lowercase_		:	Optional[Any]=1     ,  lowercase_		:	Optional[Any]=False					)   ->		Tuple:
             """simple docstring"""




             if word in self.indices and not overwrite:
                    _lowerCamelCase			:						Any            =self.indices[word]
                    _lowerCamelCase			:						str            =self.count[idx] + n
                    return idx
             else:
                    _lowerCamelCase			:						List[str]            =len(self.symbols					)
                    _lowerCamelCase			:						List[str]            =idx
                    self.symbols.append(UpperCamelCase__					)
                    self.count.append(UpperCamelCase__					)
                    return idx






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	Dict					)   ->		int:
             """simple docstring"""




             return 0






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	Any					)   ->		Union[str, Any]:
             """simple docstring"""




             if isinstance(UpperCamelCase__     ,  UpperCamelCase__					):
                    try:
                           with open(UpperCamelCase__     ,  'r'     ,  encoding='utf-8'					) as fd:
                                  self.add_from_file(UpperCamelCase__					)
                    except FileNotFoundError as fnfe:
                           raise fnfe
                    except UnicodeError:
                           raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(UpperCamelCase__					)					)
                    return

             _lowerCamelCase			:						Tuple            =f.readlines()
             _lowerCamelCase			:						Dict            =self._load_meta(UpperCamelCase__					)

             for line in lines[indices_start_line:]:
                    try:
                           _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =line.rstrip().rsplit(' '     ,  1					)
                           if field == "#fairseq:overwrite":
                                  _lowerCamelCase			:						Union[str, Any]            =True
                                  _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =line.rsplit(' '     ,  1					)
                           else:
                                  _lowerCamelCase			:						List[Any]            =False
                           _lowerCamelCase			:						List[Any]            =int(UpperCamelCase__					)
                           _lowerCamelCase			:						Optional[int]            =line
                           if word in self and not overwrite:
                                  raise RuntimeError(
                                      'Duplicate word found when loading Dictionary: \'{}\'. '
                                      'Duplicate words can overwrite earlier ones by adding the '
                                      '#fairseq:overwrite flag at the end of the corresponding row '
                                      'in the dictionary file. If using the Camembert model, please '
                                      'download an updated copy of the model file.'.format(UpperCamelCase__					)					)
                           self.add_symbol(UpperCamelCase__     ,  n=UpperCamelCase__     ,  overwrite=UpperCamelCase__					)
                    except ValueError:
                           raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''					)




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       _lowerCamelCase			:						Tuple            =dict((re.sub(r'@@$'						,     ''						,     A_	), v) if k.endswith('@@'	) else (re.sub(r'$'						,     '</w>'						,     A_	), v) for k, v in d.items()	)
       _lowerCamelCase			:						Any            ='<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'''{k}</w>''']
              _lowerCamelCase			:						int            =d[k]  # restore
       return da




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if not os.path.exists(A_	):
              raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''	)
       os.makedirs(A_						,     exist_ok=A_	)
       print(F'''Writing results to {pytorch_dump_folder_path}'''	)

       # handle various types of models

       _lowerCamelCase			:						List[str]            =os.path.join(A_						,     'checkpoint.pt'	)
       if not os.path.isfile(A_	):
              raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''	)
       _lowerCamelCase			:						Optional[int]            =torch.load(A_						,     map_location='cpu'	)

       _lowerCamelCase			:						Optional[int]            =chkpt['cfg']['model']

       # dicts
       _lowerCamelCase			:						Any            =os.path.join(A_						,     'dict.txt'	)
       if not os.path.isfile(A_	):
              raise ValueError(F'''path to the file {dict_file} does not exist!'''	)
       _lowerCamelCase			:						int            =Dictionary.load(A_	)
       _lowerCamelCase			:						Dict            =rewrite_dict_keys(src_dict.indices	)
       _lowerCamelCase			:						int            =len(A_	)
       _lowerCamelCase			:						List[str]            =os.path.join(A_						,     VOCAB_FILES_NAMES['vocab_file']	)
       print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''	)
       with open(A_						,     'w'						,     encoding='utf-8'	) as f:
              f.write(json.dumps(A_						,     ensure_ascii=A_						,     indent=A_	)	)

       # merges_file (bpecodes)
       _lowerCamelCase			:						Dict            =os.path.join(A_						,     'bpecodes'	)
       if not os.path.isfile(A_	):
              raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''	)

       _lowerCamelCase			:						Union[str, Any]            =os.path.join(A_						,     VOCAB_FILES_NAMES['merges_file']	)
       shutil.copyfile(A_						,     A_	)

       # model config
       _lowerCamelCase			:						Any            =os.path.join(A_						,     'config.json'	)

       _lowerCamelCase			:						List[str]            ={
           'activation_dropout': args['activation_dropout'],
           'architectures': ['BioGptForCausalLM'],
           'attention_probs_dropout_prob': args['attention_dropout'],
           'bos_token_id': 0,
           'eos_token_id': 2,
           'hidden_act': args['activation_fn'],
           'hidden_dropout_prob': args['dropout'],
           'hidden_size': args['decoder_embed_dim'],
           'initializer_range': 0.02,
           'intermediate_size': args['decoder_ffn_embed_dim'],
           'layer_norm_eps': 1e-12,
           'layerdrop': args['decoder_layerdrop'],
           'max_position_embeddings': args['max_target_positions'],
           'model_type': 'biogpt',
           'num_attention_heads': args['decoder_attention_heads'],
           'num_hidden_layers': args['decoder_layers'],
           'pad_token_id': 1,
           'scale_embedding': not args['no_scale_embedding'],
           'tie_word_embeddings': args['share_decoder_input_output_embed'],
           'vocab_size': src_vocab_size,
       }

       # good hparam defaults to start with

       print(F'''Generating {biogpt_model_config_file}'''	)
       with open(A_						,     'w'						,     encoding='utf-8'	) as f:
              f.write(json.dumps(A_						,     ensure_ascii=A_						,     indent=A_	)	)

       # tokenizer config
       _lowerCamelCase			:						Optional[Any]            =os.path.join(A_						,     A_	)

       _lowerCamelCase			:						List[Any]            ={
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1_024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

       print(F'''Generating {biogpt_tokenizer_config_file}'''	)
       with open(A_						,     'w'						,     encoding='utf-8'	) as f:
              f.write(json.dumps(A_						,     ensure_ascii=A_						,     indent=A_	)	)

       # model
       _lowerCamelCase			:						int            =chkpt['model']

       # remove unneeded keys
       _lowerCamelCase			:						int            =[
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(A_						,     A_	)

       _lowerCamelCase			:						int            =list(model_state_dict.keys()	)
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'	):
                     _lowerCamelCase			:						Optional[int]            =model_state_dict.pop(A_	)
              else:
                     _lowerCamelCase			:						int            =model_state_dict.pop(A_	)

       _lowerCamelCase			:						int            =BioGptConfig.from_pretrained(A_	)
       _lowerCamelCase			:						List[Any]            =BioGptForCausalLM(A_	)

       # check that it loads ok
       model_new.load_state_dict(A_	)

       # save
       _lowerCamelCase			:						Optional[Any]            =os.path.join(A_						,     A_	)
       print(F'''Generating {pytorch_weights_dump_path}'''	)
       torch.save(A_						,     A_	)

       print('Conversion is done!'	)


if __name__ == "__main__":
 lowerCamelCase		   =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--biogpt_checkpoint_path',
     default=None,
     type=str,
     required=True,
     help=(
         'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
         ' bpecodes, etc.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 lowerCamelCase		   =			parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

